Method for checking class attendance on basis of multi-face data acquisition strategies and deep learning

Abstract

The invention discloses a method for checking class attendance on the basis of multi-face data acquisition strategies and deep learning. By the aid of the method, the technical problem of low recognition rates of existing methods for checking attendance on the basis of face recognition can be solved. The technical scheme includes that multiple objects are detected and extracted by the aid of AdaBoost algorithms and skin color models. Only a piece of video needs to be shot on every face participating in attendance checking at one step, faces in video sequences are detected and extracted, and face databases can be completely created. The method has the advantages that learning can be carried out on face features in the face databases in different scenes by the aid of simplified LeNet-5 models on the basis of depth convolutional neural network LeNet-5 models by the aid of processes for recognizing the faces on the basis of deep learning, and novel features can be represented by means of multilayer nonlinear transformation; intra-class change of illumination, noise, attitude, expression and the like is removed from the novel features as much as possible, inter-class change generated by identity difference is reserved, and accordingly the face recognition rates of the processes for recognizing the faces in practical complicated scenes can be increased.

Description

technical field [0001] The invention relates to an attendance method based on face recognition, in particular to a classroom attendance method based on a multi-face data collection strategy and deep learning. Background technique [0002] The document "A Prototype of Automated Attendance System Using ImageProcessing, International Journal of Advanced Research in Computer and Communication Engineering, Vol.5, Issue 4, April 2016, p501-505" discloses an attendance method based on face recognition. This method uses the traditional principal component analysis method to recognize the detected faces. After the attendee enters the attendance system, the system determines whether the face data exists in the database. If it exists, it will directly identify it and add the detection result to the database. If it does not exist, it needs to collect face data first. The realization of this method needs to collect the face data of the attendees one by one before recognition. However,...

AI Technical Summary

Problems solved by technology

[0003] In order to overcome the shortcomings of the poor recognition rate of existing attendance methods based on face recognition, the present invention provides a classroom attendance method based on multi-face data collection strategy and deep learning

It solves the problem of time-consuming and labor-intensive face data collection in actual attendance, and it is difficult to collect uniformly, and it is easier to obtain massive face data

Images